MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Why MCP?
MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
A growing list of pre-built integrations that your LLM can directly plug into
The flexibility to switch between LLM providers and vendors
Best practices for securing your data within your infrastructure
General architecture
At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:
Internet
Your Computer
MCP Protocol
MCP Protocol
MCP Protocol
Web APIs
Host with MCP Client
(Claude, IDEs, Tools)
MCP Server A
MCP Server B
MCP Server C
Local
Data Source A
Local
Data Source B
Remote
Service C
MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
MCP Clients: Protocol clients that maintain 1:1 connections with servers
MCP Servers: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
Local Data Sources: Your computer’s files, databases, and services that MCP servers can securely access
Remote Services: External systems available over the internet (e.g., through APIs) that MCP servers can connect to
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MCP stands for Model Context Protocol. It's an open protocol that standardizes how applications provide context to Large Language Models. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Why do we need MCP? MCP helps you build agents and complex workflows on top of Large Language Models. LLMs frequently need to integrate with data and tools, and MCP provides three key benefits. First, a growing list of pre-built integrations that your LLM can directly plug into. Second, the flexibility to switch between LLM providers and vendors. And third, best practices for securing your data within your infrastructure.
At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. MCP Hosts are programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP. MCP Clients are protocol clients that maintain one-to-one connections with servers. MCP Servers are lightweight programs that each expose specific capabilities through the standardized Model Context Protocol. This architecture allows for flexible connections between AI applications and various data sources.
MCP connects to two main types of data sources. First, local data sources - these are your computer's files, databases, and services that MCP servers can securely access within your own infrastructure. Second, remote services - these are external systems available over the internet, such as web APIs, cloud databases, and external tools that MCP servers can connect to. This dual approach allows MCP to provide comprehensive access to both your private data and external resources while maintaining security boundaries.
In summary, MCP provides a standardized way to connect AI models to data and tools, much like USB-C standardizes device connections. It offers pre-built integrations that are ready to use, flexibility to switch between different LLM providers, security best practices to keep your data within your infrastructure, and a scalable architecture that can connect multiple servers and data sources. MCP is the universal connector that makes AI applications more powerful, flexible, and secure.